U.S. patent application number 17/545131 was filed with the patent office on 2022-07-28 for preventive controller switchover.
This patent application is currently assigned to ABB Schweiz AG. The applicant listed for this patent is ABB Schweiz AG. Invention is credited to Wilhelm Weise.
Application Number | 20220237100 17/545131 |
Document ID | / |
Family ID | 1000006329395 |
Filed Date | 2022-07-28 |
United States Patent
Application |
20220237100 |
Kind Code |
A1 |
Weise; Wilhelm |
July 28, 2022 |
Preventive Controller Switchover
Abstract
A preventive switchover from a primary controller to a secondary
controller even before the primary controller fails system and
method includes a server that collects log files comprising
operational parameters of the primary controller from the primary
controller in real-time. The server determines abnormal patterns or
signatures in the operational parameters of the primary controller
by comparing the operational parameters with reference patterns or
signatures. The reference patterns or signatures are generated by
training one or more Artificial Intelligence (AI) based models.
After determining the abnormal patterns or signatures, the server
predicts events that will lead to switchover from the primary
controller to the secondary controller. Thereafter, the server
provides a signal to the primary controller to perform preventive
switchover to the secondary controller before the primary
controller fails.
Inventors: |
Weise; Wilhelm; (Minden,
DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ABB Schweiz AG |
Baden |
|
CH |
|
|
Assignee: |
ABB Schweiz AG
Baden
CH
|
Family ID: |
1000006329395 |
Appl. No.: |
17/545131 |
Filed: |
December 8, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 11/2028 20130101;
G06F 2201/86 20130101; G06F 11/2025 20130101; G06F 11/3452
20130101; G06F 11/3055 20130101 |
International
Class: |
G06F 11/34 20060101
G06F011/34; G06F 11/20 20060101 G06F011/20; G06F 11/30 20060101
G06F011/30 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 9, 2020 |
EP |
20212782.5 |
Claims
1. A method of performing a redundancy switchover in a
process/industrial plant, wherein the process/industrial plant
comprises a primary controller and a secondary controller, wherein
the secondary controller is redundant to the primary controller,
wherein the primary controller is configured to operate one or more
equipment, wherein the primary controller and the secondary
controller are connected to a server, wherein the method is
performed by the server, comprising: receiving log files from the
primary controller in real-time, wherein the log files comprise a
plurality of operational parameters of the primary controller;
determining abnormal patterns or signatures in the plurality of
operational parameters of the primary controller by comparing the
plurality of operational parameters with reference patterns or
signatures, wherein the reference patterns or signatures are
generated based on one or more trained models; predicting one or
more events leading to a switchover from the primary controller to
the secondary controller based on the abnormal patterns or
signatures in the plurality of operational parameters of the
primary controller; and providing a signal to the primary
controller to perform the switchover based on the predicted one or
more events, thereby performing preventive controller switchover
from the primary controller to the secondary controller.
2. The method of claim 1, wherein the plurality of operational
parameters comprises at least one of: hardware parameters, software
parameters, firmware parameters, and network parameters.
3. The method of claim 1, wherein the one or more trained models
are Artificial Intelligence based models.
4. The method of claim 1, wherein generating the reference patterns
or signatures comprises: receiving training log files comprising
historical operational parameters of the primary controller;
detecting one or more historical events which led to switchover
from the primary controller to the secondary controller;
identifying patterns or signatures in the historical operational
parameters in the training log files corresponding to the one or
more historical events which led to the switchover; classifying
normal patterns or signatures and abnormal patterns or signatures
based on the identified patterns or signatures, wherein the
reference patterns or signatures are one of, the normal patterns or
signatures or the abnormal patterns or signatures.
5. The method of claim 1, wherein the abnormal patters or
signatures are determined and the one or more events are predicted
by the one or more trained models.
6. The method of claim 1, wherein based on the predicted one or
more events operational parameters of the secondary controller are
modified before performing the switchover from the primary
controller to the secondary controller.
7. A server for performing redundancy switchover in a
process/industrial plant, wherein the process/industrial plant
comprises a primary controller and a secondary controller, wherein
the secondary controller is redundant to the primary controller,
wherein the primary controller is configured to operate one or more
equipment, wherein the primary controller and the secondary
controller are connected to the server, wherein the server
comprises: a memory; and one or more processors configured to:
receive log files from the primary controller in real-time, wherein
the log files comprise a plurality of operational parameters of the
primary controller; determine abnormal patterns or signatures in
the plurality of operational parameters of the primary controller
by comparing the plurality of operational parameters with reference
patterns or signatures, wherein the reference patterns or
signatures are generated based on one or more trained models;
predict one or more events leading to a switchover from the primary
controller to the secondary controller based on the abnormal
patterns or signatures in the plurality of operational parameters
of the primary controller; and provide a signal to the primary
controller to perform the switchover based on the predicted one or
more events, thereby performing preventive controller switchover
from the primary controller to the secondary controller.
8. The server of claim 7, wherein the one or more trained models
are Artificial Intelligence based models.
9. The server of claim 7, wherein, for generating the reference
patterns or signatures, the one or more processors are configured
to: receive training log files comprising historical operational
parameters of the primary controller; detect one or more historical
events which led to switchover from the primary controller to the
secondary controller; identify patterns or signatures in the
historical operational parameters in the training log files
corresponding to the one or more historical events which led to the
switchover; classify normal patterns or signatures and abnormal
patterns or signatures based on the identified patterns or
signatures, wherein the reference patterns or signatures are one
of, the normal patterns or signatures or the abnormal patterns or
signatures.
10. The sever of claim 7, wherein the one or more processors are
associated with a display unit to display a notification about the
predicted one or more events.
11. A system for performing redundancy switchover in a
process/industrial plant, the system comprising: a primary
controller configured to operate one or more equipment in the
process/industrial plant; a secondary controller redundant to the
primary controller; and a server configured to: receive log files
from the primary controller in real-time, wherein the log files
comprise a plurality of operational parameters of the primary
controller; determine abnormal patterns or signatures in the
plurality of operational parameters of the primary controller by
comparing the plurality of operational parameters with reference
patterns or signatures, wherein the reference patterns or
signatures are generated based on one or more trained models;
predict one or more events leading to a switchover from the primary
controller to the secondary controller based on the abnormal
patterns or signatures in the plurality of operational parameters;
and provide a signal to the primary controller to perform the
switchover based on the predicted one or more events, thereby
performing preventive controller switchover from the primary
controller to the secondary controller.
12. The system of claim 11, wherein the one or more trained models
are Artificial Intelligence based models.
13. The system of claim 11, wherein, for generating the reference
patterns or signatures, the server is configured to: receive
training log files comprising historical operational parameters of
the primary controller; detect one or more historical events which
led to switchover from the primary controller to the secondary
controller; identify patterns or signatures in the historical
operational parameters in the training log files corresponding to
the one or more historical events which led to the switchover;
classify normal patterns or signatures and abnormal patterns or
signatures based on the identified patterns or signatures, wherein
the reference patterns or signatures are one of, the normal
patterns or signatures or the abnormal patterns or signatures.
14. The system of claim 11, further comprises a display unit to
display a notification about the predicted one or more events.
15. The system of claim 11, wherein based on the predicted one or
more events operational parameters of the secondary controller are
modified before performing the switchover from the primary
controller to the secondary controller.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Priority is claimed to European Patent Application Ser. No.
20212782.5, filed on Dec. 9, 2020, the entire disclosure of which
is hereby incorporated by reference herein.
FIELD OF THE DISCLOSURE
[0002] The present disclosure generally relates to controller
redundancy in a process/industrial plant. More specifically, the
present invention relates to predicting primary controller failure
events and performing switchover to secondary controller.
BACKGROUND OF THE DISCLOSURE
[0003] Process or industrial plants comprise a plurality of
equipment such as pumps, drives, air compressors, machinery,
electrical appliances, etc. It is essential to operate the
industrial equipment continuously to ensure maximum output of the
process or industrial plant. Current technology enables continuous
operation of the equipment to reduce downtime of the equipment
operation. Generally, the equipment is controlled by controllers,
and due to fault in controllers, the equipment operation is halted.
Typically, a redundant controller is configured as a hot stand-by
which takes over the control of the equipment when a primary
controller fails. The redundant controller may store all the
configuration settings and parameters related to operating the
equipment when switched from the primary controller. Hence, the
redundant controller helps in smooth operation of the process or
industrial plant.
[0004] In existing controller redundancy systems, the controller
switchover occurs after the primary controller has failed. However,
the cause of the primary controller failing may also occur in the
redundant controller, thus failing the redundant controller. Also,
the switchover occurs at a critical time when the primary
controller has actually failed. Often times, the primary controller
is required to transfer status and operating parameters of the
primary controller to the redundant controller. However, when the
primary controller has failed, the primary controller cannot
transfer all the required data to the redundant controller to
efficiently control the equipment. Also, delay in switching from
primary controller to redundant controller during the critical time
may add to operational failure.
[0005] Hence, there is a need to provide a preventive switchover
from a primary controller to the redundant controller before the
primary controller fails.
BRIEF SUMMARY OF THE DISCLOSURE
[0006] In an embodiment, the present disclosure relates to a
method, server and a system for performing redundancy switchover in
a process/industrial plant. In an embodiment, the
process/industrial plant comprises a primary controller and a
secondary controller. The primary controller is configured to
operate one or more equipment. The secondary controller is
redundant to the primary controller. The primary and the secondary
controllers are connected to a server. The server is configured to
receive log files from the primary controller in real-time, where
the log files comprise operational parameters of the primary
controller. Further, the server determines abnormal parameters or
signatures in the operational parameters of the primary controller
by comparing the operational parameters with reference patterns or
signatures. In an embodiment, the reference patterns or signatures
are generated based on one or more trained models. The server
further predicts one or more events leading to a switchover from
the primary controller to the secondary controller based on the
abnormal patterns or signatures in the operational parameters.
Thereafter, the server provides a signal to the primary controller
to perform switchover the primary controller to the secondary
controller based on the predicted one or more events. Therefore,
the switchover occurs even before the one or more events takes
place, thus enabling preventive controller switchover.
[0007] In an embodiment, the operational parameters comprise at
least one of, hardware parameters, software parameters, firmware
parameters and network parameters.
[0008] In an embodiment, the one or more trained models are
Artificial Intelligence (AI) based models.
[0009] In an embodiment, the server generates the reference
patterns or signatures by performing the following steps. The
server receives training log files comprising historical
operational parameters of the primary controller. Further, the
server detects one or more events which led to the switchover from
the primary controller to the secondary controller using the
historical operational parameters. Further, the server identifies
patterns or signatures in the historical parameters in the training
log files corresponding to the one or more events. Thereafter, the
server classifies normal patterns or signatures and abnormal
patterns or signatures based on the identified patterns or
signatures, where the classified patterns are stored as reference
patterns. In an embodiment, the one or more trained models
determines the abnormal patterns or signatures and predicts the one
or more events in real-time.
[0010] In an embodiment, the predicted one or more events are
displayed on a display unit.
[0011] Systems of varying scope are described herein. In addition
to the aspects and advantages described in this summary, further
aspects and advantages will become apparent by reference to the
drawings and with reference to the detailed description that
follows.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0012] Subject matter of the present disclosure will be described
in even greater detail below based on the exemplary figures. All
features described and/or illustrated herein can be used alone or
combined in different combinations. The features and advantages of
various embodiments will become apparent by reading the following
detailed description with reference to the attached drawings, which
illustrate the following:
[0013] FIG. 1 illustrates controller redundancy in a
process/industrial plant, in accordance with some embodiments of
the present invention;
[0014] FIG. 2 illustrates internal architecture of a server for
performing preventive switchover in a process/industrial plant, in
accordance with some embodiments of the present invention;
[0015] FIG. 3a illustrates training and inference stages of models
for generating reference patterns or signatures, in accordance with
some embodiments of the present invention;
[0016] FIG. 3b illustrates an exemplary diagram of generating
reference patterns or signatures, in accordance with some
embodiments of the present invention
[0017] FIG. 4a illustrates an exemplary flowchart for performing
redundancy switchover in a process/industrial plant, in accordance
with some embodiments of the present invention;
[0018] FIG. 4b illustrates an exemplary diagram of predicting
events in the primary controller, in accordance with some
embodiments of the present invention;
[0019] FIG. 5 illustrates an exemplary scenario of normal operation
of a primary controller in a process/industrial plant, in
accordance with some embodiments of the present invention; and
[0020] FIG. 6 illustrates an exemplary scenario of abnormal
operation of a primary controller in a process/industrial plant, in
accordance with some embodiments of the present invention.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT
[0021] Embodiments of the present invention relates to performing
preventive switchover from a primary controller to a secondary
controller even before the primary controller fails. A server
collects log files comprising operational parameters of the primary
controller from the primary controller in real-time. Further, the
server determines abnormal patterns or signatures in the
operational parameters of the primary controller by comparing the
operational parameters with reference patterns or signatures. The
reference patterns or signatures are generated by training one or
more Artificial Intelligence (AI) based models. After determining
the abnormal patterns or signatures, the server predicts events
that will lead to switchover from the primary controller to the
secondary controller. Thereafter, the server provides a signal to
the primary controller to perform preventive switchover to the
secondary controller before the primary controller fails.
Therefore, the abnormality in the operational parameters is
notified and precaution is taken when secondary controller takes
over such that the abnormalities do not occur in the secondary
controller.
[0022] FIG. 1 illustrates controller redundancy in a
process/industrial plant. The process/industrial plant comprises
one or more equipment (104). Examples of equipment includes, but
are not limited to, a motor, a drive, a pump, machinery, and the
like. The one or more equipment (104) is/are controlled by a
primary controller (102a). In an exemplary embodiment, the primary
controller (102a) may be connected to the one or more equipment
(104) via industry standard communication protocols, examples
including but not limited to, Ethernet, RS232, and RS485. Further,
the primary controller (102a) may be connected to a server (101)
via a network (103). The network (103) may support Ethernet LAN,
WAN, Wi-Fi, and the like. In an embodiment, the server (101) may be
part of a Distributed Control System (DC S) or a Supervisory
Control and Data Acquisition (SCADA) system, or a standalone
system. In an embodiment, the server (101) is configured to acquire
data of the process/industrial plant and perform analytics on the
acquired data. For example, the server (101) may acquire data of
the processes, the one or more equipment (104), and the primary
controller (102a). In an embodiment, the server (101) may be
replaced with a personal computer, a laptop, a mobile, or any other
electronic device which is capable of analyzing a plurality of
operational parameters of the primary controller (102a) and
predicting failure condition in the primary controller (102a).
[0023] In an embodiment, a secondary controller (102b) is
commissioned as a redundant controller in the process/industrial
plant. In order to reduce downtime due to failure of the primary
controller (102a), the secondary controller (102b) is used as the
redundant controller. In an embodiment, the secondary controller
(102b) is connected to the one or more equipment (104) and to the
server (101). In an embodiment, the primary controller (102a) and
the secondary controller (102b) may be connected via a redundancy
link. The redundancy link may support Ethernet protocol. Other
protocols known in the art may be used as the redundancy link as
well. Conventionally, when the primary controller (102a) fails, the
switchover occurs from the primary controller (102a) to the
secondary controller (102b). The primary controller (102a)
initiates the redundancy switchover to the secondary controller
(102b). Generally, the primary controller (102a) transfers the
plurality of operational parameters and a status of the one or more
equipment to the secondary controller while initiating the
redundancy switchover. Therefore, when the secondary controller
(102b) takes over, the operation of the process/industrial plant
can resume from where the primary controller (102a) had failed. In
an embodiment, reasons for failure of the primary controller (102a)
can include, but not limited to, hardware faults, software faults,
firmware faults, and network faults. In an embodiment, the
secondary controller (102b) may be connected to the server (101)
via network (103) different from the network used for connecting
the primary controller (102a) to the server (101). Therefore, when
the primary controller (102a) has failed due to network fault, the
secondary controller (102b) is secure from the network fault.
[0024] Reference is now made to FIG. 2. FIG. 2 illustrates internal
architecture of the server (101). The server comprises one or more
processors (201), a memory (202) and an Input/Output (I/O)
interface (203). The I/O interface (203) provisions connection
between the server (101), and the primary controller (102a) and the
secondary controller (102b). The I/O interface (203) may also
provision connection between the server (101) and the DCS or SCADA.
In an embodiment, the memory (202) stores the plurality of
operational parameters of the primary controller (102a). In an
embodiment, the memory (202) may be a database. In an embodiment,
the one or more processors (201) is configured to receive the
plurality of operational parameters of the primary controller
(102a) in real-time. In an embodiment, the one or more processors
(201) may receive the operational parameters at regular intervals.
In one embodiment, the one or more parameters may be time series
data. The one or more processors (201) determines abnormal patterns
or signatures in the plurality of operational parameters by
comparing the plurality of operational parameters with reference
patterns or signatures. The reference patterns or signatures are
generated by one or more trained models (AI models (204)). Further,
the one or more models (204) predicts one or more events that leads
to failure of the primary controller (102a), which eventually leads
to switchover from the primary controller (102a) to the secondary
controller (102b). Thereafter, the one or more processors (201)
provides a signal to the primary controller (102a) to perform a
switchover from the primary controller (102a) to the secondary
controller (102b) based on the predicted one or more events.
Therefore, the switchover is performed even before the primary
controller (102a) fails, which leads to a preventive controller
switchover. The preventive controller switchover enables smooth
switchover. Also, as the predictive controller switchover occurs
before the primary controller (102a) fails, the criticality of the
switchover is reduced, thus providing more efficient switchover to
the secondary controller (102b). The advantages can be realized in
view of further embodiments which are explained along with
examples.
[0025] FIG. 3a illustrates training and inference stages of one or
more models (204) for generating reference patterns or signatures.
The one or more models (204) is/are implemented in the server
(101), and the one or more models (204) is/are trained to generate
the reference patterns or signatures.
[0026] At step (301) the one or more models (204) receive training
log files comprising historical operational parameters of the
primary controller (102a). The training log files may be obtained
from one or more historians associated with the process/industrial
plant. The training logs may be selected which comprises the
historical operational parameters which led to the switchover from
the primary controller (102a) to the secondary controller
(102b).
[0027] At step (302) the one or more models (204) detect one or
more historical events that led to the switchover from the primary
controller (102a) to the secondary controller (102b). In an
embodiment, a domain expert may label the historical operational
parameters with the one or more historical events. The one or more
events are failure events in the primary controller (102a). In an
embodiment, the domain expert may label for few historical
operational parameters and train the one or more models (204) to
label for a large set of historical operational parameters. For
example, the domain expert may label a first set of historical
operational parameters with the label "memory fault," a second set
of historical operational parameters with the label "communication
port fault," a third set of historical parameters with the label
"null zero fault," a fourth set of historical operational
parameters with a label "communication fault." In an embodiment,
each type of controller failure (historical events) may be
diagnosed and a root cause may be associated. For example, the
failure "memory fault" may be associated with a root cause
"insufficient memory," the failure "null zero fault" may be
associated with a root cause "incorrect dynamic memory allocation,"
the failure "communication fault" may be associated with a root
cause "firmware incompatibility," and the failure "communication
port fault" may be associated with the root cause "burnt port." The
above examples should not be considered as limitations and other
examples commonly known to a person skilled in the art are also
envisaged with the instant invention.
[0028] At step (303), the one or more models (204) identify
patterns or signatures in the historical operational parameters
corresponding to the one or more historical events. In an
embodiment, the one or more models (204) may include, but not
limited to classification models, regression models or any other
type of models which are capable of determining patterns or
signatures in the historical operational parameters. In some
embodiment, the patterns may refer to variations in the historical
operational parameters before the one or more historical events
occurred in the primary controller (102a). For example, there may
be specific pattern in the historical operational parameters when a
"memory fault' occurred. There may be specific variations in the
values of the historical operational parameters that led to the
"memory fault" in the primary controller. The one or more models
(204) may require time series data to identify the patterns. In
some embodiments, the signatures may also be identified. Not always
patterns may lead to the failure in the primary controller (102a).
A specific variation in the historical operational parameters may
also have led to the one or more historical events. Such specific
variation can be considered as signatures, which may not need time
series data, but data at a specific time (for example one value of
an operational parameter). In one embodiment, the domain expert may
have labelled few historical operational parameters while the one
or more models (204) may use the labels to further label the
historical operational parameters which may comprise huge data set.
An auto-labeller may be used to label the historical operational
parameters. The Auto-labeller may be an AI based model. In an
embodiment, the one or more models (204) may also determine time
taken for the primary controller (102a) failed from the identified
patterns or signatures. In some embodiment, the one or more models
(204) may map the time value against each of the one or more
events.
[0029] At step (304) the one or more models (204) classifies the
identified patterns or signatures into normal patterns or
signatures and abnormal patterns or signatures. In one embodiment,
the domain expert may also label few historical operational
parameters with normal patterns and abnormal patterns. The one or
more models (204) may be trained to label the historical
operational parameters with the normal patterns and the abnormal
patterns using the labels provided by the domain expert. In an
embodiment, the abnormal patterns may be an entire region after a
certain point in the time series data. In an embodiment, the
abnormal signature may be a point value in the historical
operational parameters. It is apparent to a person skilled in the
art that point values or time series data can be used to observe
the abnormal signatures in the primary controller (102a).
Therefore, it will also be apparent to the person skilled in the
art how the AI models are trained and used for inferring the
abnormal behavior of the primary controller (102a). In an
embodiment, the classification models may be used for classifying
into normal patterns and abnormal patterns. In an embodiment, the
normal patterns or abnormal patterns may be stored as reference
patterns. For example, in process/industrial plants where the one
or more historical events are certain and are known to the domain
experts, the abnormal patterns may be stored as reference patterns.
In process/industrial plants where the one or more historical
events are uncertain and are generally not known to the domain
experts, the normal patterns may be stored as reference patterns.
The reference patterns are compared with real-time operational
parameters of the primary controller (102a) to predict the one or
more events. In an embodiment, the training of the one or more
models (204) is concluded when the one or more models (204) have a
defined efficiency.
[0030] FIG. 3b shows an example diagram of generating the reference
patterns or signatures. As shown, the one or more models (204)
receive the historical operational parameters as inputs.
Optionally, the one or more models (204) may also receive labels as
inputs. In an embodiment, the one or more models (204) may cluster
the different types of events and label the clusters without the
domain expert providing the labels as input. Further, the one or
more models (204) generate the reference patterns or signatures
using the historical operational parameters as described in FIG.
3a. The reference patterns or signatures are then saved in the
memory (202).
[0031] FIG. 4a illustrates an exemplary flowchart for performing
redundancy switchover in a process/industrial plant. The following
steps are performed in real-time.
[0032] At step (401) the server (101) receives log files comprising
the operational parameters of the primary controller (102a) in
real-time. In an embodiment, the operational parameters may be
received at regular time intervals (for example every 5 minutes).
In an embodiment, the operational parameters are received during
normal operation of the primary controller (102a). When the primary
controller (102a) is operating normally, the secondary controller
(102b) is in hot stand-by. The operational parameters includes
hardware parameters, software parameters, firmware parameters and
network parameters.
[0033] At step (402) the server (101) determines abnormal patterns
or signatures in the operational parameters by comparing the
operational parameters with the reference patterns or signatures.
In an embodiment, the server (101) may compare the operational
parameters with reference abnormal patterns or signatures or with
reference normal patterns or signatures. When comparing the
operational parameters with normal patterns or signatures, the
abnormal patterns or signatures in the operational parameters is
determined when the values of the operational parameters deviate
from the normal patterns or signatures. When comparing the
operational parameters with abnormal patterns or signatures, the
abnormal patterns or signatures in the operational parameters is
determined when the values of the operational parameters match the
abnormal patterns or signatures.
[0034] At step (403) the server (101) predicts the one or more
events leading to switchover from the primary controller (102a) to
the secondary controller (102b). The one or more events are the
events that lead to failure of the primary controller (102a). In an
embodiment, the one or more trained models are used to predict the
one or more events. The one or more trained models may have
associated the events with the reference patterns or the
signatures. Using the association, the one or more models (204)
predicts the one or more events in real time. For example, the one
or more models (204) may identify abnormal patterns in operational
parameters related to memory of the primary controller (102a) and
predict that a memory fault may occur. In an embodiment, the one or
more models (204) may also predict time to failure of the primary
controller (012a). For example, the one or more models (204) may
predict that the primary controller (102a) may fail after 5 minutes
from detecting the abnormal patterns in the operational parameters
related to the memory. FIG. 5 illustrates an exemplary scenario
where the primary controller (102a) is operating normally and the
secondary controller (102b) is in hot stand-by, while the server
(101) receives the operational parameters of the primary controller
(102a).
[0035] At step (404) the server (101) provides a signal to the
primary controller (102a) to perform switchover to the secondary
controller (102b). In an embodiment, the server (101) provides the
signal to the primary controller (102a) even before the primary
controller (102a) fails. Hence, the switchover from the primary
controller (102a) to the secondary controller (102b) occurs before
the primary controller (102a) fails. Thus, the problems associated
with switchover after the primary controller (102a) fails is
avoided. When the primary controller (102a) receives the signal
from the server (101) the primary controller (102a) transfers its
operational parameters along with the status associated with the
equipment (104). Further, the secondary controller (102b) controls
the equipment (104) and the primary controller (102a) is inactive
and may be scheduled for maintenance. In an embodiment, when the
one or more models (204) predict the one or more events, a
notification may be provided to indicate the one or more events. An
operator may attend the notification and resolve the one or more
events before the switchover so that the one or more events do not
occur in the secondary controller (102b). In an embodiment, the
operational parameters of the secondary controller (102b) may be
modified to avoid the one or more events from occurring in the
secondary controller (102b). For example, in case of "memory fault"
the operator may provide the secondary controller (102b) with
additional memory. Hence, the predicted event of "memory fault"
does not occur in the secondary controller (102b).
[0036] As shown in FIG. 4b the one or more models (204) are
provided with the real-time operational parameters of the primary
controller (102a). Since the one or more models (204) are trained
and have generated the reference patterns or signatures, the one or
more models (204) compare the operational parameters to the
reference patterns or signatures. Based on the comparison, the one
or more models (204) predict the one or more events (failure
events) in the primary controller (102a), and the signal is
provided to the primary controller (102a) to perform the preventive
switchover to the secondary controller (102b) even before the
primary controller (102a) fails.
[0037] Therefore, the signal provided by the server (101) enable
preventive switchover from the primary controller (102a) to the
secondary controller (102b). In an embodiment, as the fault in the
primary controller (102a) does not occur, logs comprising failure
events are reduced and such logs can be closed. FIG. 6 illustrates
a scenario where the secondary controller (102b) controls the
equipment (104) and the primary controller (102a) is inactive.
[0038] This written description uses examples to describe the
subject matter herein, including the best mode, and also to enable
any person skilled in the art to make and use the subject matter.
The patentable scope of the subject matter is defined by the
claims, and may include other examples that occur to those skilled
in the art. Such other examples are intended to be within the scope
of the claims if they have structural elements that do not differ
from the literal language of the claims, or if they include
equivalent structural elements with insubstantial differences from
the literal language of the claims.
[0039] All references, including publications, patent applications,
and patents, cited herein are hereby incorporated by reference to
the same extent as if each reference were individually and
specifically indicated to be incorporated by reference and were set
forth in its entirety herein.
[0040] The use of the terms "a" and "an" and "the" and "at least
one" and similar referents in the context of describing the
invention (especially in the context of the following claims) are
to be construed to cover both the singular and the plural, unless
otherwise indicated herein or clearly contradicted by context. The
use of the term "at least one" followed by a list of one or more
items (for example, "at least one of A and B") is to be construed
to mean one item selected from the listed items (A or B) or any
combination of two or more of the listed items (A and B), unless
otherwise indicated herein or clearly contradicted by context. The
terms "comprising," "having," "including," and "containing" are to
be construed as open-ended terms (i.e., meaning "including, but not
limited to,") unless otherwise noted. Recitation of ranges of
values herein are merely intended to serve as a shorthand method of
referring individually to each separate value falling within the
range, unless otherwise indicated herein, and each separate value
is incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the invention and does not
pose a limitation on the scope of the invention unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the invention.
[0041] Preferred embodiments of this invention are described
herein, including the best mode known to the inventors for carrying
out the invention. Variations of those preferred embodiments may
become apparent to those of ordinary skill in the art upon reading
the foregoing description. The inventors expect skilled artisans to
employ such variations as appropriate, and the inventors intend for
the invention to be practiced otherwise than as specifically
described herein. Accordingly, this invention includes all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the invention unless otherwise
indicated herein or otherwise clearly contradicted by context.
* * * * *